I recently discovered your collection of packages and think this is a wonderful project.
I looked through the pre-existing sample designs and did not find any that would match a relatively common (yet, fraught) concern:
Detecting causal effects with panel data
Currently, most people use the plm package for estimation - which is fantastic in its own right. However, I was wondering if the DeclareDesign suite can handle this concern, since the combination of fabricatr, estimatr, and DeclareDesign would be fantastic.
For example, I am assessing how elections impact trust in government at the county level with 27 years of data.
In your framework, I can see the following parameters for the setup:
Population: There is a stable population of 3082 counties
Repeated Measurement: There is a balanced panel with data for 27 years per county
Population: Assume, this entire population can be split into either Republican or Democrat counties (where assignment stays constant, i.e. they are considered either Republican or Democrat for all 27 years)
Random Assignment: Assume, every four years, an election occurs which is considered to be a truly “exogenous shock” i.e. all counties of a given party are randomly assigned as “winning election” (e.g Dem County - Dem President) or “losing election” (e.g. Rep County - Dem President) 
Causal Model to be Tested: Right after winning an election, “trust in government” increases - and then slowly falls back to baseline (and, vice versa for losing an election).
 Of course, incumbency effects would change the probability of being assigned to winning or losing, but presumably this can be added at a later stage.
Based upon the above, would you believe that:
- This kind of setup is easily / natively modeled with the DeclareDesign framework?
- If so, once modeled, am I correct that the fabricatr package could be used for power calculations?
- And, could the estimatr package be used for robust estimation of effects?
- Finally, can the estimatr package be used to address Difference-in-Difference models under such dependency constraints?
If the above is actually true, would you mind pointing me in the right direction on how to tackle this setup?
I would be happy to share the final outcome as a “Design Template” - since such setups are increasingly common in political science and policy frameworks and hopefully could be useful to others as well.
Highlighting some points of concern regarding panel data:
- For accurate inference, standard errors must be clustered at the appropriate level (e.g., County-level or State-level clusters)
- In most panel data, there are time-fixed effects (due to external factors, e.g. Hurricane Katrina or 2007 banking crisis)
- It is necessary to account for cross-sectional dependencies
- It is necessary to account for serial correlation at the county level (as well as differing levels of geographical hierarchy, e.g. State, Census Region) and block level (e.g. Republican / Democrat counties may experience unique changes over time)